{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q8vHMOtbxH4y"
      },
      "source": [
        "Copyright 2021 Google LLC\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "you may not use this file except in compliance with the License.\n",
        "You may obtain a copy of the License at\n",
        "\n",
        "     https://www.apache.org/licenses/LICENSE-2.0\n",
        "\n",
        "Unless required by applicable law or agreed to in writing, software\n",
        "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "See the License for the specific language governing permissions and\n",
        "limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "txNTHnugxKGe"
      },
      "source": [
        "# Colab demonstration of KIP and Label Solve algorithms"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nHXSlyfOxRiL"
      },
      "source": [
        "This colab implements the KIP (Kernel Inducing Point) and Label Solve algorithms introduced in the paper [Dataset Metalearning from Kernel Ridge-Regression](https://arxiv.org/abs/2011.00050). These algorithms allow for dataset distillation, which produces a small subset of learned images and/or labels which when trained upon perform as well as a much larger dataset for image classification classification tasks. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ETZ1YWxfn_q2"
      },
      "source": [
        "# Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "executionInfo": {
          "elapsed": 53,
          "status": "ok",
          "timestamp": 1666582866399,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 420
        },
        "id": "eAsNM11umxLz"
      },
      "outputs": [],
      "source": [
        "import dataclasses\n",
        "import functools\n",
        "from typing import Callable, Optional\n",
        "\n",
        "from jax.example_libraries import optimizers\n",
        "import jax\n",
        "from jax import config as jax_config\n",
        "jax_config.update('jax_enable_x64', True) # for numerical stability, can disable if not an issue\n",
        "from jax import numpy as jnp\n",
        "from jax import scipy as sp\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "executionInfo": {
          "elapsed": 56,
          "status": "ok",
          "timestamp": 1648091584243,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 420
        },
        "id": "8RRu_vG2nbjF",
        "outputId": "32efe100-ad4e-40d9-c74f-a2ede82182b1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/bin/sh: line 1: pip: command not found\n"
          ]
        }
      ],
      "source": [
        "!pip install -q git+https://www.github.com/google/neural-tangents"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6wgJY0hqncuM"
      },
      "outputs": [],
      "source": [
        "import neural_tangents as nt\n",
        "from neural_tangents import stax"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L5MsuE5poiWH"
      },
      "source": [
        "# Define Parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lbyEeP8FxOKM"
      },
      "outputs": [],
      "source": [
        "# architecture params\n",
        "ARCHITECTURE = 'FC' #@param ['FC', 'Conv', 'Myrtle']; choice of neural network architecture yielding the corresponding NTK\n",
        "DEPTH =  1#@param {'type': int}; depth of neural network\n",
        "WIDTH = 1024 #@param {'type': int}; width of finite width neural network; only used if parameterization is 'standard'\n",
        "PARAMETERIZATION = 'ntk' #@param ['ntk', 'standard']; whether to use standard or NTK parameterization, see https://arxiv.org/abs/2001.07301\n",
        "\n",
        "# dataset\n",
        "DATASET = 'cifar10' #@param ['cifar10', 'cifar100', 'mnist', 'svhn_cropped']\n",
        "\n",
        "# training params\n",
        "LEARNING_RATE = 4e-2 #@param {'type': float};\n",
        "SUPPORT_SIZE = 100  #@param {'type': int}; number of images to learn\n",
        "TARGET_BATCH_SIZE = 5000  #@param {'type': int}; number of target images to use in KRR for each step\n",
        "LEARN_LABELS = False #@param {'type': bool}; whether to optimize over support labels during training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J_MMTsKRoNyR"
      },
      "source": [
        "# Load Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HgF2vzW1oPZF"
      },
      "outputs": [],
      "source": [
        "def get_tfds_dataset(name):\n",
        "  ds_train, ds_test = tfds.as_numpy(\n",
        "      tfds.load(\n",
        "          name,\n",
        "          split=['train', 'test'],\n",
        "          batch_size=-1,\n",
        "          as_dataset_kwargs={'shuffle_files': False}))\n",
        "\n",
        "  return ds_train['image'], ds_train['label'], ds_test['image'], ds_test['label']\n",
        "\n",
        "def one_hot(x,\n",
        "            num_classes,\n",
        "            center=True,\n",
        "            dtype=np.float32):\n",
        "  assert len(x.shape) == 1\n",
        "  one_hot_vectors = np.array(x[:, None] == np.arange(num_classes), dtype)\n",
        "  if center:\n",
        "    one_hot_vectors = one_hot_vectors - 1. / num_classes\n",
        "  return one_hot_vectors\n",
        "\n",
        "def get_normalization_data(arr):\n",
        "  channel_means = np.mean(arr, axis=(0, 1, 2))\n",
        "  channel_stds = np.std(arr, axis=(0, 1, 2))\n",
        "  return channel_means, channel_stds\n",
        "\n",
        "def normalize(array, mean, std):\n",
        "  return (array - mean) / std\n",
        "\n",
        "def unnormalize(array, mean, std):\n",
        "  return (array * std) + mean\n",
        "\n",
        "\n",
        "X_TRAIN_RAW, LABELS_TRAIN, X_TEST_RAW, LABELS_TEST = get_tfds_dataset(DATASET)\n",
        "channel_means, channel_stds = get_normalization_data(X_TRAIN_RAW)\n",
        "X_TRAIN, X_TEST = normalize(X_TRAIN_RAW, channel_means, channel_stds), normalize(X_TEST_RAW, channel_means, channel_stds) \n",
        "Y_TRAIN, Y_TEST = one_hot(LABELS_TRAIN, 10), one_hot(LABELS_TEST, 10) "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UqU9zsUsEUHQ"
      },
      "source": [
        "Augmentation class used to augment images"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "executionInfo": {
          "elapsed": 110,
          "status": "ok",
          "timestamp": 1666583024526,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 420
        },
        "id": "DNdLyttkEYY2"
      },
      "outputs": [],
      "source": [
        "@dataclasses.dataclass\n",
        "class Augmentor:\n",
        "  \"\"\"Class for creating augmentation function.\"\"\"\n",
        "\n",
        "  # function applied after augmentations (maps uint8 image to float image)\n",
        "  # if standard preprocessing, this should be function which does channel-wise\n",
        "  # standardization\n",
        "  preprocessing_function: Callable[[np.ndarray], np.ndarray]\n",
        "\n",
        "  # need this to unnormalize images if they are already normalized\n",
        "  # before applying augmentations\n",
        "  channel_means: Optional[np.ndarray] = None\n",
        "  channel_stds: Optional[np.ndarray] = None\n",
        "\n",
        "  # Specify these to augment at custom rate\n",
        "  augmentation_frac: float = 1.0\n",
        "  rotation_range: float = 0.0\n",
        "  width_shift_range: float = 0.0\n",
        "  height_shift_range: float = 0.0\n",
        "  horizontal_flip: bool = False\n",
        "  channel_shift_range: float = 0.0\n",
        "\n",
        "  def __post_init__(self):\n",
        "    self.aug_generator = tf.keras.preprocessing.image.ImageDataGenerator(\n",
        "        rotation_range=self.rotation_range,\n",
        "        width_shift_range=self.width_shift_range,\n",
        "        height_shift_range=self.height_shift_range,\n",
        "        horizontal_flip=self.horizontal_flip,\n",
        "        channel_shift_range=self.channel_shift_range,\n",
        "        preprocessing_function=self.preprocessing_function,\n",
        "    )\n",
        "\n",
        "  def __call__(self,\n",
        "               x: np.ndarray,\n",
        "               normalized: bool = True,\n",
        "               seed: Optional[int] = None):\n",
        "    \"\"\"Augments a numpy array of images.\n",
        "\n",
        "    Args:\n",
        "      x: image array (B,H,W,C)\n",
        "      normalized: if True, then image is assumed to be standard preprocessed and\n",
        "        therefore must be unnormalized before augmented\n",
        "      seed: random seed for augmentations\n",
        "\n",
        "    Returns:\n",
        "      augmented images\n",
        "    \"\"\"\n",
        "\n",
        "    if self.augmentation_frac == 0.0:\n",
        "      return x\n",
        "\n",
        "    permutation = np.random.permutation(x.shape[0])\n",
        "    inv_permutation = get_inverse_permutation(permutation)\n",
        "    num_aug_images = int(self.augmentation_frac * x.shape[0])\n",
        "\n",
        "    x = x[permutation]\n",
        "    if normalized:\n",
        "      x_raw = unnormalize(x, self.channel_means, self.channel_stds)\n",
        "    else:\n",
        "      x_raw = x\n",
        "\n",
        "    iterator = self.aug_generator.flow(  # pytype: disable=attribute-error\n",
        "        x_raw[:num_aug_images],\n",
        "        batch_size=num_aug_images,\n",
        "        shuffle=False,\n",
        "        seed=seed)\n",
        "    x_aug = next(iterator)\n",
        "    x_aug = np.concatenate([x_aug, x[num_aug_images:]])\n",
        "    return x_aug[inv_permutation]\n",
        "\n",
        "def get_inverse_permutation(perm):\n",
        "  array = np.zeros_like(perm)\n",
        "  array[perm] = np.arange(len(perm), dtype=int)\n",
        "  return array"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w8yR-mNeoP4K"
      },
      "source": [
        "# Define Kernel"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9sLlI8URoTGv"
      },
      "outputs": [],
      "source": [
        "# define architectures\n",
        "\n",
        "def FullyConnectedNetwork( \n",
        "    depth,\n",
        "    width,\n",
        "    W_std = np.sqrt(2), \n",
        "    b_std = 0.1,\n",
        "    num_classes = 10,\n",
        "    parameterization = 'ntk',\n",
        "    activation = 'relu'):\n",
        "  \"\"\"Returns neural_tangents.stax fully connected network.\"\"\"\n",
        "  activation_fn = stax.Relu()\n",
        "  dense = functools.partial(\n",
        "      stax.Dense, W_std=W_std, b_std=b_std, parameterization=parameterization)\n",
        "\n",
        "  layers = [stax.Flatten()]\n",
        "  for _ in range(depth):\n",
        "    layers += [dense(width), activation_fn]\n",
        "  layers += [stax.Dense(num_classes, W_std=W_std, b_std=b_std, \n",
        "                        parameterization=parameterization)]\n",
        "\n",
        "  return stax.serial(*layers)\n",
        "\n",
        "def FullyConvolutionalNetwork( \n",
        "    depth,\n",
        "    width,\n",
        "    W_std = np.sqrt(2), \n",
        "    b_std = 0.1,\n",
        "    num_classes = 10,\n",
        "    parameterization = 'ntk',\n",
        "    activation = 'relu'):\n",
        "  \"\"\"Returns neural_tangents.stax fully convolutional network.\"\"\"\n",
        "  activation_fn = stax.Relu()\n",
        "  conv = functools.partial(\n",
        "      stax.Conv,\n",
        "      W_std=W_std,\n",
        "      b_std=b_std,\n",
        "      padding='SAME',\n",
        "      parameterization=parameterization)\n",
        "\n",
        "  for _ in range(depth):\n",
        "    layers += [conv(width, (3,3)), activation_fn]\n",
        "  layers += [stax.Flatten(), stax.Dense(num_classes, W_std=W_std, b_std=b_std,\n",
        "                                        parameterization=parameterization)]\n",
        "\n",
        "  return stax.serial(*layers)\n",
        "\n",
        "def MyrtleNetwork(  \n",
        "    depth,\n",
        "    width,\n",
        "    W_std = np.sqrt(2), \n",
        "    b_std = 0.1,\n",
        "    num_classes = 10,\n",
        "    parameterization = 'ntk',\n",
        "    activation = 'relu'):\n",
        "  \"\"\"Returns neural_tangents.stax Myrtle network.\"\"\"\n",
        "  layer_factor = {5: [1, 1, 1], 7: [1, 2, 2], 10: [2, 3, 3]}\n",
        "  if depth not in layer_factor.keys():\n",
        "    raise NotImplementedError(\n",
        "        'Myrtle network withd depth %d is not implemented!' % depth)\n",
        "  activation_fn = stax.Relu()\n",
        "  layers = []\n",
        "  conv = functools.partial(\n",
        "      stax.Conv,\n",
        "      W_std=W_std,\n",
        "      b_std=b_std,\n",
        "      padding='SAME',\n",
        "      parameterization=parameterization)\n",
        "  layers += [conv(width, (3, 3)), activation_fn]\n",
        "\n",
        "  # generate blocks of convolutions followed by average pooling for each\n",
        "  # layer of layer_factor except the last\n",
        "  for block_depth in layer_factor[depth][:-1]:\n",
        "    for _ in range(block_depth):\n",
        "      layers += [conv(width, (3, 3)), activation_fn]\n",
        "    layers += [stax.AvgPool((2, 2), strides=(2, 2))]\n",
        "\n",
        "  # generate final blocks of convolution followed by global average pooling\n",
        "  for _ in range(layer_factor[depth][-1]):\n",
        "    layers += [conv(width, (3, 3)), activation_fn]\n",
        "  layers += [stax.GlobalAvgPool()]\n",
        "\n",
        "  layers += [\n",
        "      stax.Dense(num_classes, W_std, b_std, parameterization=parameterization)\n",
        "  ]\n",
        "\n",
        "  return stax.serial(*layers)\n",
        "\n",
        "def get_kernel_fn(architecture, depth, width, parameterization):\n",
        "  if architecture == 'FC':\n",
        "    return FullyConnectedNetwork(depth=depth, width=width, parameterization=parameterization)\n",
        "  elif architecture == 'Conv':\n",
        "    return FullyConvolutionalNetwork(depth=depth, width=width, parameterization=parameterization)\n",
        "  elif architecture == 'Myrtle':\n",
        "    return MyrtleNetwork(depth=depth, width=width, parameterization=parameterization)\n",
        "  else:\n",
        "    raise NotImplementedError(f'Unrecognized architecture {architecture}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xvlwOxjC8I_9"
      },
      "outputs": [],
      "source": [
        "_, _, kernel_fn = get_kernel_fn(ARCHITECTURE, DEPTH, WIDTH, PARAMETERIZATION)\n",
        "KERNEL_FN = jax.jit(functools.partial(kernel_fn, get='ntk'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hEOv9lzuoTv8"
      },
      "source": [
        "# Run KIP"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XHfPTAAmrBIR"
      },
      "outputs": [],
      "source": [
        "def class_balanced_sample(sample_size: int, \n",
        "                          labels: np.ndarray,\n",
        "                          *arrays: np.ndarray, **kwargs: int):\n",
        "  \"\"\"Get random sample_size unique items consistently from equal length arrays.\n",
        "\n",
        "  The items are class_balanced with respect to labels.\n",
        "\n",
        "  Args:\n",
        "    sample_size: Number of elements to get from each array from arrays. Must be\n",
        "      divisible by the number of unique classes\n",
        "    labels: 1D array enumerating class label of items\n",
        "    *arrays: arrays to sample from; all have same length as labels\n",
        "    **kwargs: pass in a seed to set random seed\n",
        "\n",
        "  Returns:\n",
        "    A tuple of indices sampled and the corresponding sliced labels and arrays\n",
        "  \"\"\"\n",
        "  if labels.ndim != 1:\n",
        "    raise ValueError(f'Labels should be one-dimensional, got shape {labels.shape}')\n",
        "  n = len(labels)\n",
        "  if not all([n == len(arr) for arr in arrays[1:]]):\n",
        "    raise ValueError(f'All arrays to be subsampled should have the same length. Got lengths {[len(arr) for arr in arrays]}')\n",
        "  classes = np.unique(labels)\n",
        "  n_classes = len(classes)\n",
        "  n_per_class, remainder = divmod(sample_size, n_classes)\n",
        "  if remainder != 0:\n",
        "    raise ValueError(\n",
        "        f'Number of classes {n_classes} in labels must divide sample size {sample_size}.'\n",
        "    )\n",
        "  if kwargs.get('seed') is not None:\n",
        "    np.random.seed(kwargs['seed'])\n",
        "  inds = np.concatenate([\n",
        "      np.random.choice(np.where(labels == c)[0], n_per_class, replace=False)\n",
        "      for c in classes\n",
        "  ])\n",
        "  return (inds, labels[inds].copy()) + tuple(\n",
        "      [arr[inds].copy() for arr in arrays])\n",
        "\n",
        "def make_loss_acc_fn(kernel_fn):\n",
        "\n",
        "  @jax.jit\n",
        "  def loss_acc_fn(x_support, y_support, x_target, y_target, reg=1e-6):\n",
        "    y_support = jax.lax.cond(LEARN_LABELS, lambda y: y, jax.lax.stop_gradient, y_support)\n",
        "    k_ss = kernel_fn(x_support, x_support)\n",
        "    k_ts = kernel_fn(x_target, x_support)\n",
        "    k_ss_reg = (k_ss + jnp.abs(reg) * jnp.trace(k_ss) * jnp.eye(k_ss.shape[0]) / k_ss.shape[0])\n",
        "    pred = jnp.dot(k_ts, sp.linalg.solve(k_ss_reg, y_support, assume_a='pos'))\n",
        "    mse_loss = 0.5*jnp.mean((pred - y_target) ** 2)\n",
        "    acc = jnp.mean(jnp.argmax(pred, axis=1) == jnp.argmax(y_target, axis=1))\n",
        "    return mse_loss, acc\n",
        "\n",
        "  return loss_acc_fn\n",
        "\n",
        "def get_update_functions(init_params, kernel_fn, lr):\n",
        "  opt_init, opt_update, get_params = optimizers.adam(lr) \n",
        "  opt_state = opt_init(init_params)\n",
        "  loss_acc_fn = make_loss_acc_fn(kernel_fn)\n",
        "  value_and_grad = jax.value_and_grad(lambda params, x_target, y_target: loss_acc_fn(params['x'],\n",
        "                                                                       params['y'],\n",
        "                                                                       x_target,\n",
        "                                                                       y_target), has_aux=True)\n",
        "\n",
        "  @jax.jit\n",
        "  def update_fn(step, opt_state, params, x_target, y_target):\n",
        "    (loss, acc), dparams = value_and_grad(params, x_target, y_target)\n",
        "    return opt_update(step, dparams, opt_state), (loss, acc)\n",
        "\n",
        "  return opt_state, get_params, update_fn\n",
        "\n",
        "def train(num_train_steps, log_freq=20, seed=1):\n",
        "\n",
        "  _, labels_init, x_init_raw, y_init = class_balanced_sample(SUPPORT_SIZE, LABELS_TRAIN, X_TRAIN_RAW, Y_TRAIN, seed=seed)\n",
        "  x_init = normalize(x_init_raw, channel_means, channel_stds)\n",
        "  params_init = {'x': x_init, 'y': y_init}\n",
        "  params_init_raw = {'x': x_init_raw, 'y': labels_init}\n",
        "\n",
        "  opt_state, get_params, update_fn = get_update_functions(params_init, KERNEL_FN, LEARNING_RATE)\n",
        "  params = get_params(opt_state)\n",
        "  loss_acc_fn = make_loss_acc_fn(KERNEL_FN)\n",
        "\n",
        "  test_loss, test_acc = loss_acc_fn(params['x'], params['y'], X_TEST, Y_TEST)  # compute in batches for expensive kernels\n",
        "  print('initial test loss:', test_loss)\n",
        "  print('initial test acc:', test_acc)\n",
        "\n",
        "  for i in range(1,num_train_steps+1):\n",
        "    # full batch gradient descent\n",
        "    _, _, x_target_batch, y_target_batch = class_balanced_sample(TARGET_BATCH_SIZE, LABELS_TRAIN, X_TRAIN, Y_TRAIN) \n",
        "    opt_state, aux = update_fn(i, opt_state, params, x_target_batch, y_target_batch)\n",
        "    train_loss, train_acc = aux\n",
        "    params = get_params(opt_state)\n",
        "    if i % log_freq == 0:\n",
        "      print(f'----step {i}:')\n",
        "      print('train loss:', train_loss)\n",
        "      print('train acc:', train_acc)\n",
        "      test_loss, test_acc = loss_acc_fn(params['x'], params['y'], X_TEST, Y_TEST)  # compute in batches for expensive kernels\n",
        "      print('test loss:', test_loss)\n",
        "      print('test acc:', test_acc)\n",
        "\n",
        "  return params, params_init, params_init_raw"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T6AufNvmf5Y6"
      },
      "source": [
        "## Run KIP to learn SUPPORT_SIZE=100 number of images using FC1 kernel. Algorithm converges rapidly (less than 300 training steps needed in below run for reasonable performance). Train for ~ 1000 or more steps for full convergence."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "executionInfo": {
          "elapsed": 63337,
          "status": "ok",
          "timestamp": 1648091662138,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 420
        },
        "id": "GGH8WKdi8CZY",
        "outputId": "ba089432-b311-4515-a05a-1362a91e1437"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "initial test loss: 0.04881845089133273\n",
            "initial test acc: 0.2426\n",
            "----step 20:\n",
            "train loss: 0.03785347938059138\n",
            "train acc: 0.4174\n",
            "test loss: 0.038064773899372226\n",
            "test acc: 0.4141\n",
            "----step 40:\n",
            "train loss: 0.03720814099932076\n",
            "train acc: 0.443\n",
            "test loss: 0.037338622156861706\n",
            "test acc: 0.4293\n",
            "----step 60:\n",
            "train loss: 0.036885772039689256\n",
            "train acc: 0.4424\n",
            "test loss: 0.03694839343346532\n",
            "test acc: 0.4385\n",
            "----step 80:\n",
            "train loss: 0.036357592223034536\n",
            "train acc: 0.4636\n",
            "test loss: 0.036742799753910226\n",
            "test acc: 0.44680000000000003\n",
            "----step 100:\n",
            "train loss: 0.03561979973071187\n",
            "train acc: 0.48140000000000005\n",
            "test loss: 0.03655465004514848\n",
            "test acc: 0.4544\n",
            "----step 120:\n",
            "train loss: 0.0360803397686207\n",
            "train acc: 0.4716\n",
            "test loss: 0.03634762176594858\n",
            "test acc: 0.4587\n",
            "----step 140:\n",
            "train loss: 0.035626511132304264\n",
            "train acc: 0.4818\n",
            "test loss: 0.03626113668588657\n",
            "test acc: 0.45730000000000004\n",
            "----step 160:\n",
            "train loss: 0.03544180547524741\n",
            "train acc: 0.4844\n",
            "test loss: 0.03618743229650087\n",
            "test acc: 0.45690000000000003\n",
            "----step 180:\n",
            "train loss: 0.03538212838532755\n",
            "train acc: 0.48140000000000005\n",
            "test loss: 0.03606212472143797\n",
            "test acc: 0.4667\n",
            "----step 200:\n",
            "train loss: 0.03510253543330575\n",
            "train acc: 0.49860000000000004\n",
            "test loss: 0.035933045936505835\n",
            "test acc: 0.46230000000000004\n",
            "----step 220:\n",
            "train loss: 0.03524867255675284\n",
            "train acc: 0.4914\n",
            "test loss: 0.03591529501529448\n",
            "test acc: 0.4685\n",
            "----step 240:\n",
            "train loss: 0.03475615993083446\n",
            "train acc: 0.496\n",
            "test loss: 0.03585655368629501\n",
            "test acc: 0.4657\n",
            "----step 260:\n",
            "train loss: 0.03495109246979981\n",
            "train acc: 0.5044000000000001\n",
            "test loss: 0.035803287117138684\n",
            "test acc: 0.4696\n",
            "----step 280:\n",
            "train loss: 0.03483083418457401\n",
            "train acc: 0.5038\n",
            "test loss: 0.03583452892875892\n",
            "test acc: 0.4675\n",
            "----step 300:\n",
            "train loss: 0.0348403179230418\n",
            "train acc: 0.4964\n",
            "test loss: 0.03576224950209431\n",
            "test acc: 0.46740000000000004\n"
          ]
        }
      ],
      "source": [
        "params_final, params_init, params_init_raw = train(300)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "height": 662
        },
        "executionInfo": {
          "elapsed": 3609,
          "status": "ok",
          "timestamp": 1648091665928,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 420
        },
        "id": "vtqrqHoYRDpE",
        "outputId": "529983a8-a32d-4a77-e09e-2812db742352"
      },
      "outputs": [
        {
          "data": {
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z28y+E8AfIbqA/qmFN60ATC/8RaGZfRLA9tPVTQ0h/LWZPQvAH5nZ9YhqCVYR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MaU3pjIq4X94IMPU2ygh/el7Rt4LNJwagbnCrwtBvs5T9odPpY368nzmE6Zt/XAWt6Xyut4vW2+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\ngOzTGY9DlvnyZmedkQctSzu7XYZNEvEe4+6PcIe1SDm1HXazddk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WrHVyE9Y7uQVrndyC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\nABDEVAAAAAAAAIAgpgIAAAAAAACECXlakfIAygKCAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "\u003cFigure size 3300x1000 with 30 Axes\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "_, _, sample_raw, sample_init, sample_final = class_balanced_sample(10, params_init_raw['y'], params_init_raw['x'], params_init['x'], params_final['x'], seed=3)\n",
        "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
        "\n",
        "fig = plt.figure(figsize=(33,10))\n",
        "fig.suptitle('Image comparison.\\n\\nRow 1: Original uint8.  Row2: Original normalized.  Row 3: KIP learned images.', fontsize=16, y=1.02)\n",
        "for i, img in enumerate(sample_raw):\n",
        "  ax = plt.subplot(3, 10, i+1)\n",
        "  ax.set_title(class_names[i])\n",
        "  plt.imshow(np.squeeze(img))\n",
        "\n",
        "for i, img in enumerate(sample_init, 1):\n",
        "  plt.subplot(3, 10, 10+i)\n",
        "  plt.imshow(np.squeeze(img))\n",
        "\n",
        "for i, img in enumerate(sample_final, 1):\n",
        "  plt.subplot(3, 10, 20+i)\n",
        "  plt.imshow(np.squeeze(img))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7mu-uhruoWRQ"
      },
      "source": [
        "# Run Label Solve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0P47zfIGoV3I"
      },
      "outputs": [],
      "source": [
        "def make_label_solve_fn(kernel_fn):\n",
        "\n",
        "  @jax.jit\n",
        "  def label_solve(x_support, x_target, y_target, reg=1e-6):\n",
        "    \"\"\"Formula for label solve valid when |x_support| \u003c= |x_target|. A regularized version of the pseudo-inverse is used for numerical stability.\"\"\"\n",
        "    kss = kernel_fn(x_support, x_support)\n",
        "    kst = kernel_fn(x_support, x_target)\n",
        "    matrix = jnp.dot(kst, kst.T)\n",
        "    matrix_reg = matrix + reg * jnp.eye(matrix.shape[0])/matrix.shape[0]\n",
        "    reg_pinv = sp.linalg.solve(matrix_reg, jnp.dot(kst, y_target), assume_a='pos')\n",
        "    return jnp.dot(kss + reg* jnp.eye(kss.shape[0])/kss.shape[0], reg_pinv)\n",
        "\n",
        "  return label_solve"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wy82CFxFeQXx"
      },
      "source": [
        "## Label solve using 500 cifar10 images using FC1 kernel"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "executionInfo": {
          "elapsed": 8100,
          "status": "ok",
          "timestamp": 1648091698341,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 420
        },
        "id": "TNw44mmjcjrh",
        "outputId": "7c45d9a4-e3a6-476d-adb6-be9add6cac15"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Original test loss: 0.04666656574629163, test acc: 0.3211\n",
            "Label solved test loss: 0.03596384247624307, test acc: 0.46990000000000004\n"
          ]
        }
      ],
      "source": [
        "_, _, x_support, y_support = class_balanced_sample(500, LABELS_TRAIN, X_TRAIN, Y_TRAIN, seed=2021)\n",
        "solved_labels = make_label_solve_fn(KERNEL_FN)(x_support, X_TRAIN, Y_TRAIN)\n",
        "\n",
        "loss_acc_fn = make_loss_acc_fn(KERNEL_FN)\n",
        "loss_orig, acc_orig = loss_acc_fn(x_support, y_support, X_TEST, Y_TEST)\n",
        "loss_solved, acc_solved = loss_acc_fn(x_support, solved_labels, X_TEST, Y_TEST)\n",
        "print(f'Original test loss: {loss_orig}, test acc: {acc_orig}')\n",
        "print(f'Label solved test loss: {loss_solved}, test acc: {acc_solved}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "executionInfo": {
          "elapsed": 8168,
          "status": "ok",
          "timestamp": 1648091754293,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 420
        },
        "id": "2mqJVRDJWQU8",
        "outputId": "0d3bb419-0a8b-4be3-f1c2-ba2129013ffa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Original test loss: 0.046491733810580964, test acc: 0.3075\n",
            "Label solved test loss: 0.03588253104846279, test acc: 0.4693\n"
          ]
        }
      ],
      "source": [
        "inds = np.random.choice(50000, 500)\n",
        "x_support, y_support = X_TRAIN[inds], Y_TRAIN[inds]\n",
        "solved_labels = make_label_solve_fn(KERNEL_FN)(x_support, X_TRAIN, Y_TRAIN)\n",
        "\n",
        "loss_acc_fn = make_loss_acc_fn(KERNEL_FN)\n",
        "loss_orig, acc_orig = loss_acc_fn(x_support, y_support, X_TEST, Y_TEST)\n",
        "loss_solved, acc_solved = loss_acc_fn(x_support, solved_labels, X_TEST, Y_TEST)\n",
        "print(f'Original test loss: {loss_orig}, test acc: {acc_orig}')\n",
        "print(f'Label solved test loss: {loss_solved}, test acc: {acc_solved}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "height": 264
        },
        "executionInfo": {
          "elapsed": 301,
          "status": "ok",
          "timestamp": 1648091760898,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 420
        },
        "id": "lBUgEqBwW-b4",
        "outputId": "7e9404e1-80a6-47e2-8d37-ada46a301370"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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gZEIp+opoCdVB0p9JM0O/yJcOIa3J2rOk/XYkByZ6pEXRqzRbWjIM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\nCDpKOKEgCDpKOKEgCDrK/wf9l2YQ1F7icwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "\u003cFigure size 300x300 with 2 Axes\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(3, 3))\n",
        "im = ax.imshow(np.dot(y_support.T, solved_labels)/solved_labels.shape[0], \n",
        "               vmin=-0.01, vmax=0.01)\n",
        "ax.set_xticks(range(10))\n",
        "ax.set_yticks(range(10))\n",
        "ax.set_xticklabels(class_names, rotation = 90, ha=\"right\")\n",
        "ax.set_yticklabels(class_names, rotation = 0, ha=\"right\")\n",
        "ax_divider = make_axes_locatable(ax)\n",
        "# Add an axes to the right of the main axes.\n",
        "cax = ax_divider.append_axes(\"right\", size=\"7%\", pad=\"2%\")\n",
        "fig.colorbar(im, cax=cax)\n",
        "ax.set_title('FC1 LS 500 support')\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "last_runtime": {
        "build_target": "//learning/deepmind/dm_python:dm_notebook3",
        "kind": "private"
      },
      "name": "KIP_open_source.ipynb",
      "provenance": [
        {
          "file_id": "1vYM0v5F_nqbR-dY9gb2_2jBn7r0s9AoM",
          "timestamp": 1613773941658
        }
      ]
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
